
import os
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
from scipy import stats
import seaborn as sns
import random
import glob
import sweetviz as sv
import plotly.express as px

pd.options.display.float_format = '{:,}'.format
#pd.set_option('display.max_rows', 50)


data = [] # pd.concat takes a list of dataframes as an agrument
    
globbed_files = glob.glob("./measurements/jakov/**/*.csv", recursive=True) #creates a list of all csv files

for csv in globbed_files:
    frame = pd.read_csv(csv)
    frame['measured'] = os.path.basename(csv)
    frame['by'] = 'Jakov'
    full_folder_path = os.path.dirname(csv)
    frame['device'] = os.path.basename(full_folder_path)
    data.append(frame)
    
globbed_files1 = glob.glob("./measurements/johannes/**/*.csv", recursive=True) #creates a list of all csv files

for csv in globbed_files1:
    frame = pd.read_csv(csv)
    frame['measured'] = os.path.basename(csv)
    frame['by'] = 'Johannes'
    full_folder_path = os.path.dirname(csv)
    frame['device'] = os.path.basename(full_folder_path)
    data.append(frame)
    
globbed_files2 = glob.glob("./measurements/patrick/**/*.csv", recursive=True) #creates a list of all csv files

for csv in globbed_files2:
    frame = pd.read_csv(csv)
    frame['measured'] = os.path.basename(csv)
    frame['by'] = 'Patrick'
    full_folder_path = os.path.dirname(csv)
    frame['device'] = os.path.basename(full_folder_path)
    data.append(frame)

df_measurements = pd.concat(data, ignore_index=True)

# removing .csv at the end of column measured
df_measurements['measured'] = df_measurements['measured'].str[:-4]

# splitting column measured and adding 2 new columns for it in our dataframe
new = df_measurements['measured'].str.split("_", n = -1, expand = True)
  
df_measurements['measured_model']= new[0]
df_measurements['measured_compiler']= new[1]
  
# Dropping old column measured
#df_measurements.drop(columns =["measured"], inplace = True)

#df_measurements.to_csv("df_measurements.csv")


df_measurements


df_max_bandwidth = pd.read_csv(os.path.join('./','max_mem_bandwidth.csv'), delimiter=',')
df_max_bandwidth


df_measurements = df_measurements.merge(df_max_bandwidth, on='device', how='left')


df_measurements.loc[df_measurements['device']=='Intel i5-6600K']


df_measurements['percent of max bandwidth'] = ((df_measurements['bandwidth [B/s]']/(1000*1000)) / df_measurements['device max memory bandwidth [MB/s]'])


df_measurements.loc[df_measurements['device']=='Intel i5-6600K']
df_measurements.head(50)


my_report = sv.analyze(df_measurements)
#my_report.show_notebook()

                                             |          | [  0%]   00:00 -> (? left)


df_measurements.groupby(["measured"]).mean().sort_values(by='bandwidth [B/s]', ascending=False)


df_measurements.groupby(["device"]).mean().sort_values(by='bandwidth [B/s]', ascending=False)


df_measurements.groupby(["function"]).mean().sort_values(by='bandwidth [B/s]', ascending=False)


from matplotlib import cm

fig, ax = plt.subplots(figsize=(9,6))

#colors = ['b','r','c','y']
labels_cat = df_measurements['device'].unique()
nrof_labels = len(labels_cat)
colors = cm.rainbow(np.linspace(0, 1, nrof_labels))     # create a bunch of colors

for i, r in df_measurements.iterrows():
    #print(int(np.where(devices == r['device'])[0]))
    ax.plot(r['measured'], r['bandwidth [B/s]'], 'o', markersize=3, color=colors[int(np.where(labels_cat == r['device'])[0])], linewidth=0.1, label=r['device'])

#Limit Y-AXIS Range
#ax.set_ylim(0, 40000000000)

plt.scatter(x=df_measurements['measured'], y=df_measurements['bandwidth [B/s]'])
plt.xticks(rotation=90)
plt.title("Bandwith [B/s] per measurement by device")

handles, labels = plt.gca().get_legend_handles_labels()
by_label = dict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys(), loc='best')

plt.show()


# Plot absolute bamdwith valies per measurement by device
fig = px.scatter(df_measurements, x='measured', y='bandwidth [B/s]', color='device', hover_data=['function'], height=600, title = "Bandwith per measurement by device").update(layout=dict(title=dict(x=0.5)))
fig.show()


# Plot relative bamdwith valies per measurement by device
fig = px.scatter(df_measurements, x='measured', y='percent of max bandwidth', color='device', hover_data=['function'], height=600, title = "Bandwith per measurement by device").update(layout=dict(title=dict(x=0.5)))
fig.show()


# Plot absolute bamdwith values per measurement by compiler
fig = px.scatter(df_measurements, x=['measured_model', 'measured_compiler'], y='bandwidth [B/s]', color='measured_compiler', hover_data=['function'], height=600, title = "Bandwith per measurement by compiler").update(layout=dict(title=dict(x=0.5)))
fig.show()


# Plot relative bamdwith values per measurement by compiler
fig = px.scatter(df_measurements, x='measured_model', y='percent of max bandwidth', color='measured_compiler', hover_data=['function'], height=600, title = "Bandwith per measurement by compiler").update(layout=dict(title=dict(x=0.5)))
fig.show()


# Plot absolute bamdwith values per function by device
fig = px.box(df_measurements, x="function", y="bandwidth [B/s]", color='device', hover_data=['device', 'measured'], height=600, title = "Bandwith per function by device").update(layout=dict(title=dict(x=0.5)))
fig.show()


# Plot relative bamdwith values per function by device
fig = px.box(df_measurements, x="function", y='percent of max bandwidth', color='device', hover_data=['device', 'measured'], height=600, title = "Bandwith per function by device").update(layout=dict(title=dict(x=0.5)))
fig.show()


# Violin Plot absolute bamdwith values per function by device
fig = px.violin(df_measurements, x="function", y="bandwidth [B/s]", color='device', hover_data=['device', 'measured'], height=600, title = "Bandwith per function by device", box=True, points='all').update(layout=dict(title=dict(x=0.5)))
fig.show()


# Violin Plot relative bamdwith values per function by device
fig = px.violin(df_measurements, x="function", y='percent of max bandwidth', color='device', hover_data=['device', 'measured'], height=600, title = "Bandwith per function by device", box=True, points='all').update(layout=dict(title=dict(x=0.5)))
fig.show()


# Plot absolute bamdwith values per device by function
fig = px.bar(df_measurements, x="device", y="bandwidth [B/s]", color="function", height=600, title = "Bandwith per device by function").update(layout=dict(title=dict(x=0.5)))
fig.show()


# Plot relative bamdwith values per device by function
fig = px.bar(df_measurements, x="device", y="percent of max bandwidth", color="function", height=600, title = "Bandwith per device by function").update(layout=dict(title=dict(x=0.5)))
fig.show()


# Define pivot table for absolute bandwidth
pivot = pd.pivot_table(df_measurements, values=['bandwidth [B/s]'], 
                                index=['measured'], 
                                columns=['device'], 
                                aggfunc=np.mean,
                                dropna=True, fill_value=0,
                                margins=False).astype(int)
pivot = pivot.div(1024*1024).rename(columns={'bandwidth [B/s]': 'bandwidth [MB/s]'})


#Define styles for pivot table

th_props = [
  ('font-size', '13px'),
  ('text-align', 'center'),
  ('font-weight', 'bold'),
  ('color', '#6d6d6d'),
  ('background-color', '#f7f7f9')
  ]

# Set CSS properties for td elements in dataframe
td_props = [
  ('font-size', '14px')
  ]

styles = [
  dict(selector="th", props=th_props),
  dict(selector="td", props=td_props)
  ]

cm = sns.light_palette("blue", as_cmap=True)


# print absolute bandwidth as pivot table
pivot = pivot.style.format("{:,.0f}").background_gradient(cmap=cm, vmin=df_measurements['bandwidth [B/s]'].min()/(1000*1000), vmax=df_measurements['bandwidth [B/s]'].max()/(1000*1000)).set_table_styles(styles)
#pivot


#pivot.plot.barh(figsize=(10,7))


# Define pivot table for relative bandwidth - all functions
pivot1 = pd.pivot_table(df_measurements, values=['percent of max bandwidth'], 
                                index=['function', 'measured'], 
                                columns=['device'], 
                                aggfunc=np.mean,
                                dropna=True, fill_value=0.000001,
                                margins=False)

# print relative bandwidth as pivot table
#pivot1 = pivot1.style.format("{:,.2f}").background_gradient(cmap=cm, vmin=0.0, vmax=1.0).set_table_styles(styles)
#pivot1


# Define pivot table for relative bandwidth - add
pivot_add = pd.pivot_table(df_measurements[df_measurements['function']=='add'], values=['percent of max bandwidth'], 
                                index=['function', 'measured'], 
                                columns=['device'], 
                                aggfunc=np.mean,
                                dropna=True, fill_value=0,
                                margins=False)

# print relative bandwidth as pivot table
pivot_add = pivot_add.style.format("{:,.2f}").background_gradient(cmap=cm, vmin=0.0, vmax=1.0).set_table_styles(styles)
pivot_add


# Define pivot table for relative bandwidth - copy
pivot_copy = pd.pivot_table(df_measurements[df_measurements['function']=='copy'], values=['percent of max bandwidth'], 
                                index=['function', 'measured'], 
                                columns=['device'], 
                                aggfunc=np.mean,
                                dropna=True, fill_value=0.00000000001,
                                margins=False)

# print arelative bandwidth as pivot table
pivot_copy = pivot_copy.style.format("{:,.2f}").background_gradient(cmap=cm, vmin=0.0, vmax=1.0).set_table_styles(styles)
pivot_copy


# Define pivot table for relative bandwidth - dot
pivot_dot = pd.pivot_table(df_measurements[df_measurements['function']=='dot'], values=['percent of max bandwidth'], 
                                index=['function', 'measured'], 
                                columns=['device'], 
                                aggfunc=np.mean,
                                dropna=True, fill_value=0,
                                margins=False)

# print relative bandwidth as pivot table
pivot_dot = pivot_dot.style.format("{:,.2f}").background_gradient(cmap=cm, vmin=0.0, vmax=1.0).set_table_styles(styles)
pivot_dot


# Define pivot table for relative bandwidth - mul
pivot_mul = pd.pivot_table(df_measurements[df_measurements['function']=='mul'], values=['percent of max bandwidth'], 
                                index=['function', 'measured'], 
                                columns=['device'], 
                                aggfunc=np.mean,
                                dropna=True, fill_value=0,
                                margins=False)

# print relative bandwidth as pivot table
pivot_mul = pivot_mul.style.format("{:,.2f}").background_gradient(cmap=cm, vmin=0.0, vmax=1.0).set_table_styles(styles)
pivot_mul


# Define pivot table for relative bandwidth - triad
pivot_triad = pd.pivot_table(df_measurements[df_measurements['function']=='triad'], values=['percent of max bandwidth'], 
                                index=['function', 'measured'], 
                                columns=['device'], 
                                aggfunc=np.mean,
                                dropna=True, fill_value=0,
                                margins=False)

# print relative bandwidth as pivot table
pivot_triad = pivot_triad.style.format("{:,.2f}").background_gradient(cmap=cm, vmin=0.0, vmax=1.0).set_table_styles(styles)
pivot_triad


#Excel Export
writer = pd.ExcelWriter('Pivot-Tables.xlsx')
 
for function in pivot1.index.get_level_values(0).unique():
    temp_df = pivot1.xs(function, level=0).style.background_gradient(cmap=cm, vmin=0.0, vmax=1.0).set_table_styles(styles)
    temp_df.to_excel(writer, index_label=function.upper() ,sheet_name=function)
 
writer.save()

	function	bandwidth [B/s]	avg. execution time [s]	CI start [s]	CI end [s]	measured	by	device	measured_model	measured_compiler
0	copy	149,685,581,073.74344	0.0035866575	0.0035848686667293	0.0035884463332707	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang
1	mul	149,582,498,172.5372	0.0035891292	0.0035872047867738	0.0035910536132261	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang
2	add	154,908,663,954.01996	0.0051985883	0.0051965099591248	0.0052006666408751	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang
3	triad	154,936,563,089.00394	0.0051976521999999	0.005195512591663	0.0051997918083369	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang
4	dot	164,840,477,263.13992	0.0032569119	0.0032531867913816	0.0032606370086183	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang
...	...	...	...	...	...	...	...	...	...	...
400	copy	34,856,444,373.430725	0.0154023429999999	0.015315401942844	0.0154892840571559	ocl-stream_gcc	Patrick	Intel i7-9850H	ocl-stream	gcc
401	mul	34,994,602,039.49604	0.015341535	0.0152452606236817	0.0154378093763182	ocl-stream_gcc	Patrick	Intel i7-9850H	ocl-stream	gcc
402	add	32,824,889,262.2211	0.024533407	0.0244210518921726	0.0246457621078273	ocl-stream_gcc	Patrick	Intel i7-9850H	ocl-stream	gcc
403	triad	32,890,703,093.35985	0.0244843159999999	0.0243509745314563	0.0246176574685436	ocl-stream_gcc	Patrick	Intel i7-9850H	ocl-stream	gcc
404	dot	30,629,107,226.96685	0.017528128	0.0174120587504723	0.0176441972495276	ocl-stream_gcc	Patrick	Intel i7-9850H	ocl-stream	gcc

	device	device data rate [MT/s]	device memory width [byte]	device memory channels	device max memory bandwidth [MB/s]
0	Intel i5-6600K	NaN	64.0	2.0	25,600.0
1	NVIDIA Geforce GTX 1060 6GB	NaN	NaN	NaN	192,000.0
2	LCC2	NaN	NaN	NaN	NaN
3	Intel i7-7500U	NaN	64.0	2.0	29,872.0
4	Intel i7-2670QM	NaN	64.0	2.0	21,328.0
5	BROADCOM BCM2837	NaN	64.0	1.0	6,400.0
6	Intel i7-9850H	2,667.0	8.0	2.0	42,627.0
7	Dell Latitude E5440	1,600.0	8.0	1.0	12,800.0

	function	bandwidth [B/s]	avg. execution time [s]	CI start [s]	CI end [s]	measured	by	device	measured_model	measured_compiler	device data rate [MT/s]	device memory width [byte]	device memory channels	device max memory bandwidth [MB/s]
10	copy	14,478,756,795.05535	0.037079904	0.0370632565885836	0.0370965514114163	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0
11	mul	14,504,010,972.099382	0.0370153409999999	0.0370020602472784	0.0370286217527215	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0
12	add	16,217,595,107.75029	0.049656337	0.0495745470107336	0.0497381269892663	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0
13	triad	16,241,452,113.496778	0.049583397	0.0495531485644342	0.0496136454355658	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0
14	dot	21,512,173,007.471764	0.02495661	0.0248947036473953	0.0250185163526046	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0
15	copy	14,465,231,690.38664	0.037114574	0.0370182806612784	0.0372108673387215	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0
16	mul	14,479,229,675.112871	0.037078693	0.0369704470624992	0.0371869389375007	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0
17	add	16,266,258,919.305206	0.04950778	0.0493776298939022	0.0496379301060977	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0
18	triad	16,247,869,877.320974	0.049563812	0.0493962404980959	0.049731383501904	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0
19	dot	21,804,296,787.95031	0.024622253	0.0245423889490051	0.0247021170509949	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0
20	copy	14,420,910,050.360954	0.037228643	0.0372054983772111	0.0372517876227888	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0
21	mul	14,340,461,864.81888	0.0374374909999999	0.0373137715881308	0.0375612104118691	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0
22	add	15,990,701,360.555077	0.050360916	0.0502598522481941	0.0504619797518058	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0
23	triad	16,011,445,279.4843	0.0502956699999999	0.0502549469621336	0.0503363930378663	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0
24	dot	20,784,541,131.327984	0.0258302989999999	0.0257473246733794	0.0259132733266205	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0
25	copy	14,446,387,530.690144	0.037162987	0.0371378735430432	0.0371881004569567	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0
26	mul	14,485,436,598.767958	0.037062805	0.0370470334526278	0.0370785765473721	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0
27	add	16,171,034,334.189882	0.049799311	0.0497611920882151	0.0498374299117848	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0
28	triad	16,154,331,886.348867	0.0498507999999999	0.0497277242401085	0.0499738757598914	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0
29	dot	21,793,257,769.266754	0.024634725	0.0246037104818044	0.0246657395181955	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0
30	copy	14,427,865,364.30278	0.037210696	0.0371792730169298	0.0372421189830701	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0
31	mul	14,371,136,932.019234	0.037357581	0.0373307214342056	0.0373844405657943	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0
32	add	16,005,784,217.698092	0.0503134589999999	0.0502631883073606	0.0503637296926393	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0
33	triad	16,010,762,137.266554	0.050297816	0.0502288210414384	0.0503668109585615	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0
34	dot	21,092,069,415.800915	0.025453686	0.0254244039969093	0.0254829680030907	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0
35	copy	14,351,153,117.0835	0.037409601	0.0371664874333817	0.0376527145666182	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0
36	mul	14,305,256,881.870176	0.0375296239999999	0.0373314086875796	0.0377278393124203	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0
37	add	15,991,122,723.420158	0.050359589	0.0503127140526693	0.0504064639473307	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0
38	triad	15,993,829,246.95518	0.050351067	0.0502809528574285	0.0504211811425715	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0
39	dot	21,079,463,320.53184	0.025468908	0.025361744926028	0.0255760710739719	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0
40	copy	14,390,943,866.541737	0.0373061639999999	0.0372656772035802	0.0373466507964197	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0
41	mul	14,346,300,403.329515	0.037422255	0.0373897380222038	0.0374547719777961	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0
42	add	15,999,465,960.208311	0.050333328	0.050281532015792	0.0503851239842079	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0
43	triad	16,010,924,800.046446	0.0502973049999999	0.0502606107572322	0.0503339992427677	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0
44	dot	21,266,221,650.79661	0.025245242	0.0252111167403107	0.0252793672596892	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0
45	copy	14,424,120,434.954449	0.037220357	0.0369329762367945	0.0375077377632054	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0
46	mul	14,496,062,981.070444	0.037035636	0.0369460286199531	0.0371252433800468	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0
47	add	16,223,932,579.244411	0.0496369399999999	0.0493482532870765	0.0499256267129234	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0
48	triad	16,282,145,493.861383	0.049459475	0.0493699170373654	0.0495490329626346	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0
49	dot	21,798,882,979.172634	0.024628368	0.0244246272171212	0.0248321087828787	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0
50	copy	14,111,079,266.665646	0.0380460559999999	0.0379046982160906	0.0381874137839093	acc-stream_gcc	Jakov	Intel i5-6600K	acc-stream	gcc	NaN	64.0	2.0	25,600.0
51	mul	14,202,855,650.877483	0.037800209	0.0376571025057901	0.0379433154942098	acc-stream_gcc	Jakov	Intel i5-6600K	acc-stream	gcc	NaN	64.0	2.0	25,600.0
52	add	16,331,078,825.41596	0.049311278	0.0491615801062171	0.0494609758937828	acc-stream_gcc	Jakov	Intel i5-6600K	acc-stream	gcc	NaN	64.0	2.0	25,600.0
53	triad	16,220,326,569.210527	0.049647975	0.0494910832043516	0.0498048667956483	acc-stream_gcc	Jakov	Intel i5-6600K	acc-stream	gcc	NaN	64.0	2.0	25,600.0
54	dot	12,893,198,403.308558	0.041639855	0.0414873099714205	0.0417924000285794	acc-stream_gcc	Jakov	Intel i5-6600K	acc-stream	gcc	NaN	64.0	2.0	25,600.0

	function	bandwidth [B/s]	avg. execution time [s]	CI start [s]	CI end [s]	measured	by	device	measured_model	measured_compiler	device data rate [MT/s]	device memory width [byte]	device memory channels	device max memory bandwidth [MB/s]	percent of max bandwidth
0	copy	149,685,581,073.74344	0.0035866575	0.0035848686667293	0.0035884463332707	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang	NaN	NaN	NaN	192,000.0	0.7796124014257471
1	mul	149,582,498,172.5372	0.0035891292	0.0035872047867738	0.0035910536132261	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang	NaN	NaN	NaN	192,000.0	0.7790755113152978
2	add	154,908,663,954.01996	0.0051985883	0.0051965099591248	0.0052006666408751	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang	NaN	NaN	NaN	192,000.0	0.806815958093854
3	triad	154,936,563,089.00394	0.0051976521999999	0.005195512591663	0.0051997918083369	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang	NaN	NaN	NaN	192,000.0	0.8069612660885621
4	dot	164,840,477,263.13992	0.0032569119	0.0032531867913816	0.0032606370086183	ocl-stream_clang	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	clang	NaN	NaN	NaN	192,000.0	0.8585441524121871
5	copy	149,413,208,153.0319	0.0035931958	0.0035860294380306	0.0036003621619693	ocl-stream_gcc	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	gcc	NaN	NaN	NaN	192,000.0	0.7781937924637077
6	mul	149,010,325,762.15875	0.0036029108	0.003591091392952	0.0036147302070479	ocl-stream_gcc	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	gcc	NaN	NaN	NaN	192,000.0	0.7760954466779102
7	add	153,945,885,533.87137	0.0052311002999999	0.0052111886030155	0.0052510119969844	ocl-stream_gcc	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	gcc	NaN	NaN	NaN	192,000.0	0.80180148715558
8	triad	153,997,930,596.265	0.0052293324	0.0052101600045685	0.0052485047954314	ocl-stream_gcc	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	gcc	NaN	NaN	NaN	192,000.0	0.8020725551888802
9	dot	163,529,270,431.6968	0.0032830264	0.0032646754256609	0.003301377374339	ocl-stream_gcc	Jakov	NVIDIA Geforce GTX 1060 6GB	ocl-stream	gcc	NaN	NaN	NaN	192,000.0	0.8517149501650876
10	copy	14,478,756,795.05535	0.037079904	0.0370632565885836	0.0370965514114163	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0	0.5655764373068496
11	mul	14,504,010,972.099382	0.0370153409999999	0.0370020602472784	0.0370286217527215	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0	0.5665629285976321
12	add	16,217,595,107.75029	0.049656337	0.0495745470107336	0.0497381269892663	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0	0.6334998088964957
13	triad	16,241,452,113.496778	0.049583397	0.0495531485644342	0.0496136454355658	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0	0.6344317231834679
14	dot	21,512,173,007.471764	0.02495661	0.0248947036473953	0.0250185163526046	omp-stream_clang	Jakov	Intel i5-6600K	omp-stream	clang	NaN	64.0	2.0	25,600.0	0.8403192581043658
15	copy	14,465,231,690.38664	0.037114574	0.0370182806612784	0.0372108673387215	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0	0.5650481129057282
16	mul	14,479,229,675.112871	0.037078693	0.0369704470624992	0.0371869389375007	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0	0.5655949091840965
17	add	16,266,258,919.305206	0.04950778	0.0493776298939022	0.0496379301060977	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0	0.6354007390353597
18	triad	16,247,869,877.320974	0.049563812	0.0493962404980959	0.049731383501904	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0	0.6346824170828506
19	dot	21,804,296,787.95031	0.024622253	0.0245423889490051	0.0247021170509949	raja-stream_clang	Jakov	Intel i5-6600K	raja-stream	clang	NaN	64.0	2.0	25,600.0	0.851730343279309
20	copy	14,420,910,050.360954	0.037228643	0.0372054983772111	0.0372517876227888	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0	0.5633167988422247
21	mul	14,340,461,864.81888	0.0374374909999999	0.0373137715881308	0.0375612104118691	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0	0.5601742915944875
22	add	15,990,701,360.555077	0.050360916	0.0502598522481941	0.0504619797518058	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0	0.6246367718966827
23	triad	16,011,445,279.4843	0.0502956699999999	0.0502549469621336	0.0503363930378663	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0	0.6254470812298555
24	dot	20,784,541,131.327984	0.0258302989999999	0.0257473246733794	0.0259132733266205	kokkos-stream_clang	Jakov	Intel i5-6600K	kokkos-stream	clang	NaN	64.0	2.0	25,600.0	0.8118961379424994
25	copy	14,446,387,530.690144	0.037162987	0.0371378735430432	0.0371881004569567	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0	0.5643120129175837
26	mul	14,485,436,598.767958	0.037062805	0.0370470334526278	0.0370785765473721	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0	0.5658373671393734
27	add	16,171,034,334.189882	0.049799311	0.0497611920882151	0.0498374299117848	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0	0.6316810286792923
28	triad	16,154,331,886.348867	0.0498507999999999	0.0497277242401085	0.0499738757598914	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0	0.6310285893105027
29	dot	21,793,257,769.266754	0.024634725	0.0246037104818044	0.0246657395181955	omp-stream_gcc	Jakov	Intel i5-6600K	omp-stream	gcc	NaN	64.0	2.0	25,600.0	0.8512991316119826
30	copy	14,427,865,364.30278	0.037210696	0.0371792730169298	0.0372421189830701	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0	0.5635884907930774
31	mul	14,371,136,932.019234	0.037357581	0.0373307214342056	0.0373844405657943	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0	0.5613725364070014
32	add	16,005,784,217.698092	0.0503134589999999	0.0502631883073606	0.0503637296926393	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0	0.6252259460038317
33	triad	16,010,762,137.266554	0.050297816	0.0502288210414384	0.0503668109585615	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0	0.6254203959869747
34	dot	21,092,069,415.800915	0.025453686	0.0254244039969093	0.0254829680030907	tbb-stream_clang	Jakov	Intel i5-6600K	tbb-stream	clang	NaN	64.0	2.0	25,600.0	0.8239089615547232
35	copy	14,351,153,117.0835	0.037409601	0.0371664874333817	0.0376527145666182	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0	0.5605919186360743
36	mul	14,305,256,881.870176	0.0375296239999999	0.0373314086875796	0.0377278393124203	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0	0.5587990969480537
37	add	15,991,122,723.420158	0.050359589	0.0503127140526693	0.0504064639473307	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0	0.6246532313836
38	triad	15,993,829,246.95518	0.050351067	0.0502809528574285	0.0504211811425715	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0	0.6247589549591868
39	dot	21,079,463,320.53184	0.025468908	0.025361744926028	0.0255760710739719	kokkos-stream_gcc	Jakov	Intel i5-6600K	kokkos-stream	gcc	NaN	64.0	2.0	25,600.0	0.8234165359582751
40	copy	14,390,943,866.541737	0.0373061639999999	0.0372656772035802	0.0373466507964197	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0	0.5621462447867865
41	mul	14,346,300,403.329515	0.037422255	0.0373897380222038	0.0374547719777961	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0	0.5604023595050591
42	add	15,999,465,960.208311	0.050333328	0.050281532015792	0.0503851239842079	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0	0.6249791390706371
43	triad	16,010,924,800.046446	0.0502973049999999	0.0502606107572322	0.0503339992427677	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0	0.6254267500018142
44	dot	21,266,221,650.79661	0.025245242	0.0252111167403107	0.0252793672596892	tbb-stream_gcc	Jakov	Intel i5-6600K	tbb-stream	gcc	NaN	64.0	2.0	25,600.0	0.8307117832342427
45	copy	14,424,120,434.954449	0.037220357	0.0369329762367945	0.0375077377632054	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0	0.5634422044904082
46	mul	14,496,062,981.070444	0.037035636	0.0369460286199531	0.0371252433800468	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0	0.5662524601980643
47	add	16,223,932,579.244411	0.0496369399999999	0.0493482532870765	0.0499256267129234	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0	0.6337473663767348
48	triad	16,282,145,493.861383	0.049459475	0.0493699170373654	0.0495490329626346	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0	0.6360213083539603
49	dot	21,798,882,979.172634	0.024628368	0.0244246272171212	0.0248321087828787	raja-stream_gcc	Jakov	Intel i5-6600K	raja-stream	gcc	NaN	64.0	2.0	25,600.0	0.851518866373931

	bandwidth [B/s]	avg. execution time [s]	CI start [s]	CI end [s]	device data rate [MT/s]	device memory width [byte]	device memory channels	device max memory bandwidth [MB/s]	percent of max bandwidth
measured
ocl-stream_clang	70,745,480,918.08618	0.016760972139999976	0.016714408867422932	0.016807535412576978	2,667.0	36.0	2.0	88,166.33333333333	0.7988119800494423
ocl-stream_gcc	70,474,343,310.62672	0.016771498979999976	0.016719020130992645	0.016823977829007252	2,667.0	36.0	2.0	88,166.33333333333	0.7973396256043055
kokkos-stream_gcc	13,073,831,297.186052	0.05326207963333331	0.053117813062414775	0.053406346204251796	2,667.0	52.8	1.8	25,165.4	0.5468884640964239
kokkos-stream_clang	12,853,061,472.331564	0.05358152289999997	0.05341230108918114	0.05375074471081876	2,667.0	52.8	1.8	25,165.4	0.5403027258125804
raja-stream_clang	12,763,367,169.404928	0.05374383943333332	0.0533705175054174	0.05411716136124916	2,667.0	52.8	1.8	25,165.4	0.5374904098065756
tbb-stream_clang	12,726,600,863.189333	0.05412013143333331	0.05396017837414821	0.05428008449251836	2,667.0	52.8	1.8	25,165.4	0.5349970237503847
tbb-stream_gcc	12,726,527,744.66404	0.05407821779999997	0.05391181473986825	0.054244620860131626	2,667.0	52.8	1.8	25,165.4	0.534910972542494
raja-stream_gcc	12,563,706,333.56919	0.05410318039999998	0.05364369620552138	0.05456266459447851	2,667.0	52.8	1.8	25,165.4	0.5310591903825421
omp-stream_gcc	12,332,993,038.098278	0.05438685149999999	0.05394096463310723	0.054832738366892696	2,667.0	52.8	1.8	25,165.4	0.5252573989104828
omp-stream_clang	12,259,609,990.322798	0.05468438496666664	0.05416307819188189	0.0552056917414514	2,667.0	52.8	1.8	25,165.4	0.5208703163875132
acc-stream_gcc	9,662,558,653.908764	0.07597938223999999	0.07580265140786493	0.07615611307213496	NaN	64.0	1.75	20,800.0	0.5026172499544456
sycl-stream_clang	9,148,552,526.212866	0.09344108959999996	0.0918057501865082	0.0950764290134917	2,667.0	50.0	1.75	25,056.75	0.39844341096361335
sycl-stream_gcc	9,129,858,862.440008	0.09394200843999995	0.09207297404270641	0.09581104283729346	2,667.0	50.0	1.75	25,056.75	0.39654292202850283
std-stream_gcc	8,307,389,739.532463	0.08375440839999994	0.08352860552191763	0.08398021127808228	NaN	64.0	1.6666666666666667	19,200.0	0.4773812126954468
std20-stream_gcc	8,247,869,331.395006	0.08442438890000001	0.08416358298591879	0.08468519481408113	NaN	64.0	1.6666666666666667	19,200.0	0.47119522835882877
std-stream_clang	8,041,950,419.660991	0.08717490884999997	0.08703016057000626	0.08731965712999366	NaN	64.0	1.6666666666666667	19,200.0	0.45808414693431854

	bandwidth [B/s]	avg. execution time [s]	CI start [s]	CI end [s]	device data rate [MT/s]	device memory width [byte]	device memory channels	device max memory bandwidth [MB/s]	percent of max bandwidth
device
NVIDIA Geforce GTX 1060 6GB	154,385,040,402.94684	0.00417685047999998	0.004168042765989999	0.00418565819400991	NaN	NaN	NaN	192,000.0	0.8040887520986815
Intel i7-9850H	21,885,307,649.333332	0.03388144631666663	0.03290997098400207	0.03485292164933117	2,667.0	8.0	2.0	42,627.0	0.513414212807219
Intel i7-7500U	17,836,637,413.732555	0.03771627312499999	0.03757636307013888	0.037856183179861044	NaN	64.0	2.0	29,872.0	0.5971022165818345
Intel i5-6600K	16,298,697,021.6314	0.04028055151111109	0.0401880799482952	0.04037302307392693	NaN	64.0	2.0	25,600.0	0.6366678524074765
Intel i7-2670QM	12,296,850,545.36677	0.05665529374285711	0.05585108190128722	0.057459505584426966	NaN	64.0	2.0	21,328.0	0.5765590090663342
LCC2	4,682,519,459.321753	0.1556495567857143	0.15508814685496236	0.1562109667164661	NaN	NaN	NaN	NaN	NaN
BROADCOM BCM2837	2,026,565,988.282948	0.04838582785714282	0.048242805408348535	0.048528850305937093	NaN	64.0	1.0	6,400.0	0.3166509356692104

	bandwidth [B/s]	avg. execution time [s]	CI start [s]	CI end [s]	device data rate [MT/s]	device memory width [byte]	device memory channels	device max memory bandwidth [MB/s]	percent of max bandwidth
function
dot	16,795,848,273.659794	0.0660093719296296	0.06499076158632504	0.06702798227293419	2,667.0	53.66153846153846	1.7846153846153847	29,732.358208955226	0.570234370068661
add	15,873,693,273.417736	0.07173441545185184	0.07140908151665358	0.07205974938705001	2,667.0	53.66153846153846	1.7846153846153847	29,732.358208955226	0.5370048922922415
triad	15,822,686,858.696	0.07186183000740737	0.07150396444457621	0.0722196955702385	2,667.0	53.66153846153846	1.7846153846153847	29,732.358208955226	0.5356195209236811
mul	14,945,654,242.751339	0.05057479766666662	0.050329291501108	0.050820303832225286	2,667.0	53.66153846153846	1.7846153846153847	29,732.358208955226	0.5047908750383547
copy	14,929,484,531.345362	0.05034957801604934	0.05008287282178514	0.05061628321031355	2,667.0	53.66153846153846	1.7846153846153847	29,732.358208955226	0.5059125372858865

		percent of max bandwidth
	device	BROADCOM BCM2837	Intel i5-6600K	Intel i7-2670QM	Intel i7-7500U	Intel i7-9850H	NVIDIA Geforce GTX 1060 6GB
function	measured
add	acc-stream_gcc	0.32	0.64	0.60	0.59	0.00	0.00
	kokkos-stream_clang	0.31	0.62	0.60	0.58	0.49	0.00
	kokkos-stream_gcc	0.32	0.62	0.60	0.58	0.54	0.00
	ocl-stream_clang	0.00	0.00	0.00	0.80	0.77	0.81
	ocl-stream_gcc	0.00	0.00	0.00	0.80	0.77	0.80
	omp-stream_clang	0.31	0.63	0.58	0.57	0.45	0.00
	omp-stream_gcc	0.31	0.63	0.60	0.56	0.48	0.00
	raja-stream_clang	0.31	0.64	0.60	0.57	0.48	0.00
	raja-stream_gcc	0.31	0.63	0.60	0.56	0.47	0.00
	std-stream_clang	0.33	0.00	0.55	0.59	0.00	0.00
	std-stream_gcc	0.33	0.00	0.59	0.59	0.00	0.00
	std20-stream_gcc	0.32	0.00	0.58	0.59	0.00	0.00
	sycl-stream_clang	0.31	0.00	0.59	0.57	0.44	0.00
	sycl-stream_gcc	0.31	0.00	0.59	0.57	0.45	0.00
	tbb-stream_clang	0.29	0.63	0.60	0.58	0.50	0.00
	tbb-stream_gcc	0.29	0.62	0.59	0.58	0.50	0.00

		percent of max bandwidth
	device	BROADCOM BCM2837	Intel i5-6600K	Intel i7-2670QM	Intel i7-7500U	Intel i7-9850H	NVIDIA Geforce GTX 1060 6GB
function	measured
copy	acc-stream_gcc	0.38	0.55	0.53	0.53	0.00	0.00
	kokkos-stream_clang	0.33	0.56	0.55	0.54	0.48	0.00
	kokkos-stream_gcc	0.33	0.56	0.55	0.54	0.49	0.00
	ocl-stream_clang	0.00	0.00	0.00	0.84	0.82	0.78
	ocl-stream_gcc	0.00	0.00	0.00	0.84	0.82	0.78
	omp-stream_clang	0.32	0.57	0.52	0.53	0.43	0.00
	omp-stream_gcc	0.33	0.56	0.54	0.53	0.42	0.00
	raja-stream_clang	0.32	0.57	0.54	0.53	0.46	0.00
	raja-stream_gcc	0.33	0.56	0.54	0.53	0.42	0.00
	std-stream_clang	0.38	0.00	0.46	0.53	0.00	0.00
	std-stream_gcc	0.38	0.00	0.53	0.53	0.00	0.00
	std20-stream_gcc	0.38	0.00	0.53	0.53	0.00	0.00
	sycl-stream_clang	0.32	0.00	0.53	0.53	0.38	0.00
	sycl-stream_gcc	0.32	0.00	0.52	0.53	0.39	0.00
	tbb-stream_clang	0.33	0.56	0.55	0.53	0.44	0.00
	tbb-stream_gcc	0.33	0.56	0.54	0.53	0.45	0.00

		percent of max bandwidth
	device	BROADCOM BCM2837	Intel i5-6600K	Intel i7-2670QM	Intel i7-7500U	Intel i7-9850H	NVIDIA Geforce GTX 1060 6GB
function	measured
dot	acc-stream_gcc	0.31	0.50	0.60	0.43	0.00	0.00
	kokkos-stream_clang	0.31	0.81	0.84	0.80	0.63	0.00
	kokkos-stream_gcc	0.32	0.82	0.84	0.80	0.67	0.00
	ocl-stream_clang	0.00	0.00	0.00	0.78	0.72	0.86
	ocl-stream_gcc	0.00	0.00	0.00	0.78	0.72	0.85
	omp-stream_clang	0.32	0.84	0.78	0.79	0.46	0.00
	omp-stream_gcc	0.32	0.85	0.81	0.80	0.45	0.00
	raja-stream_clang	0.32	0.85	0.82	0.79	0.60	0.00
	raja-stream_gcc	0.32	0.85	0.80	0.80	0.56	0.00
	std-stream_clang	0.22	0.00	0.53	0.43	0.00	0.00
	std-stream_gcc	0.31	0.00	0.53	0.43	0.00	0.00
	std20-stream_gcc	0.29	0.00	0.52	0.43	0.00	0.00
	sycl-stream_clang	0.15	0.00	0.13	0.24	0.09	0.00
	sycl-stream_gcc	0.14	0.00	0.12	0.25	0.09	0.00
	tbb-stream_clang	0.31	0.82	0.84	0.82	0.58	0.00
	tbb-stream_gcc	0.31	0.83	0.83	0.83	0.59	0.00

		percent of max bandwidth
	device	BROADCOM BCM2837	Intel i5-6600K	Intel i7-2670QM	Intel i7-7500U	Intel i7-9850H	NVIDIA Geforce GTX 1060 6GB
function	measured
mul	acc-stream_gcc	0.29	0.55	0.54	0.54	0.00	0.00
	kokkos-stream_clang	0.33	0.56	0.54	0.53	0.47	0.00
	kokkos-stream_gcc	0.33	0.56	0.54	0.53	0.49	0.00
	ocl-stream_clang	0.00	0.00	0.00	0.83	0.82	0.78
	ocl-stream_gcc	0.00	0.00	0.00	0.83	0.82	0.78
	omp-stream_clang	0.32	0.57	0.52	0.52	0.43	0.00
	omp-stream_gcc	0.32	0.57	0.54	0.52	0.43	0.00
	raja-stream_clang	0.32	0.57	0.54	0.52	0.45	0.00
	raja-stream_gcc	0.32	0.57	0.54	0.52	0.45	0.00
	std-stream_clang	0.38	0.00	0.46	0.54	0.00	0.00
	std-stream_gcc	0.37	0.00	0.53	0.54	0.00	0.00
	std20-stream_gcc	0.36	0.00	0.52	0.54	0.00	0.00
	sycl-stream_clang	0.32	0.00	0.53	0.52	0.41	0.00
	sycl-stream_gcc	0.32	0.00	0.52	0.52	0.41	0.00
	tbb-stream_clang	0.33	0.56	0.54	0.53	0.45	0.00
	tbb-stream_gcc	0.33	0.56	0.54	0.53	0.45	0.00

505 Service Unavailable: Measurements¶